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Input parameters for CFD flow modelling of forested terrain

Boudreault, Louis-Etienne; Dellwik, Ebba; Sogachev, Andrey; Bøgh, Eva

Publication date:2012

Document VersionPublisher's PDF, also known as Version of record

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Citation (APA):Boudreault, L-E., Dellwik, E., Sogachev, A., & Bøgh, E. (2012). Input parameters for CFD flow modelling offorested terrain. Poster session presented at EWEC 2012 - European Wind Energy Conference & Exhibition,Copenhagen, Denmark.

References

Context

Input parameters for CFD flow

modeling of forested terrains Louis-Étienne Boudreault1, Ebba Dellwik1, Eva Bøgh2 , Andrey Sogachev1

1DTU Wind Energy, 2Roskilde University

- In CFD-RANS modeling, the drag effect of the forest in

the momentum equations is usually accounted trough a

sink term as follows,

where Cd = 0.2 is a drag coefficient and LAD(z) is the

vertical plant area per unit volume.

- The height of the forest and its vertical distribution

LAD(z) are thus required in order to evaluate this term.

- By applying a variety of simplification assumptions [1]

to Eq. (1), the forest architecture can be related to

atmospheric measurements by,

The purpose of this study is to present different methods for obtaining the forest characteristics required as inputs parameters in common CFD-RANS (Computational Fluid

Dynamics, Reynolds-Averaged Navier-Stokes) modeling for wind energy assessment. We compare in situ measurements of forest height h and LAI (Leaf Area Index) together

with terrestrial satellite measurements products acquired at different spatial resolutions, for which both spatial and time series were made available. It is shown that:

- For the forest height estimations, the SRTM and ASTER satellites include information from the forest, but far from the precision needed for the CFD modeling. The DSM

based on aerial laser scans underestimate the forest height by several meters, whereas the maximum height deduced from the cloud points (raw data) were in good

accordance with the forest inventory measurements (Fig. 6). Reprocessing the cloud points therefore is the most promising method for determining the forest height.

- For the LAI, the satellite-based estimates should be used with care as calibration to site specific measurements is necessary. The derived LAI values could be sensitive to a

number of environmental factors (i.e. presence of clouds or biased winter reflectance) as well as to the different scaling relations involved in its derivation. A significant

difference between the MODIS (LAI=3.1) and Landsat 7 (LAI=5.43, Fig. 7) satellites were obtained. The Landsat 7 showed unphysical variability in the derived LAI. However,

the extraction of LAD and LAI from wind data taken within the canopy (Eq. 2, LAI=5.22) showed a good comparison with the mean value of an optical in-situ technique

(LAI=5.67) and with the mean value of Landsat 7 (LAI=5.43) in the summer (Fig. 8).

Example site:

- The Tromnæs beech forest edge experiment was located

on the Falster island at 54045’ N, 1202’ E. From March-

September 2008, a measurement campaign was

conducted where both the foliated and the bare canopy

period were covered. Complex flow phenomenons were

observed making it a relevant test case for CFD validation.

Summary

EWEA 2012, Copenhagen, Denmark: Europe’s Premier Wind Energy Event

- Non-dimensional tree-specific profiles of LAD can be

constructed from the mast measurements of the wind

using Eq. (2) and the observed h:

Forest height (h) estimation

- Different canopy height estimates based on DSMs (Digital Surface Models), DTM (Digital Terrain Model) and forest inventory were obtained:

Fig. 3: Space shuttle SRTM (100 m resolution, 2000/02)

Fig. 2: Normalized beech Leaf Area Density (LAD) profiles

Fig. 1: Tromnæs forest edge experiment site. White circle: approx. mast location

Fig. 4: Satellite ASTER [2] (15-90 m resolution) Fig.5: Aerial laser-scan (1.6m resolution, 2007/05/01) Fig.6: Forest height intercomparison (forest transect)

Leaf Area Index (LAI) estimation

- The total drag of the canopy is reflected in the LAI

parameter,

- The LAI can be obtained from satellite images [3, 4]

using the reflectance ratio from different wavelength

intervals [5] or from in-situ instruments (Fig. 8).

- For the summer, the LAI values obtained from the

satellite-based MODIS instrument was 3.1 (not

shown). The Landsat 7 showed unrealistic variability

compared to the in-situ measurements (Fig 8),

although the mean values agreed well (5.43 and

5.67 respectively) with the estimate of 5.22 (point in

Fig. 8) based on Eq. (3) and the LAD summer profile

presented in Fig. 2.

1. Queck, R., Bienert, A., Maas, H.-G., Harmansa, S.,

Goldberg, V. and Bernhofer, C., 2012. Wind fields in

heterogeneous conifer canopies: parametrisation of

momentum absorption using high-resolution 3D

vegetation scans. Eur. J. Forest Res. 131:165-176.

2. ASTER GDEM is a product of METI and NASA.

3. Carlsson, T.N. and Ripley, D.A., 1997. On the relation

between NDVI, fractional vegetation cover, and leaf

area index. Remote sensing of Environment. 62(3):

241-252.

4. Zeng, X., Dickinson, R.E., Walker, A., Shaikh, M.,

DeFries, R.S. and Qi, J., 2000. Derivation and

Evaluation of Global 1-km Fractional Vegetation Cover

Data for Land Modeling. J. Appl. Meteor. 39, p. 826–

839.

5. Norman, J.M., Kustas, W.P. and Humes, K.S., 1995.

Source approach for estimating soil and vegetation

energy fluxes in observations of directional radiometric

surface temperature. Agricultural and Forest

Meteorology. 77(3-4): 263-293.

6. NASA Landsat Program, 2002, Landsat ETM+ scene

L71194022_02220020608, USGS, Sioux Falls. Fig.8: LAI intercomparison (forest transect)

PO.ID

(1)

(2)

(3)

- The use of these profiles could be extrapolated to

regions where h and LAI can be obtained from any of

the techniques shown below.

Fig.7: Satellite Landsat 7 ETM+ [6] (30m resolution, 2002/06/08)